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Measuring national innovation systems efficiency – a review of DEA approach

  • Maxim Kotsemir

    ()

    (Institute for Statistical Studies and Economics of Knowledge, National Research University — Higher School of Economics)

The paper reviews the application of the data envelopment analysis (DEA) method for measuring the efficiency of national innovation systems (NIS). The paper firstly visualizes the logic of DEA method and briefly summarizes the key advantages and main limitations of the DEA method. Further, this paper provides a comprehensive review of 11 empirical studies on cross-country analysis of NIS efficiency with DEA technique. In its main part the paper analyses the specifications of DEA models used in the reviewed studies, the content of the country samples, sets of input and output variables used and the resulting lists of efficient countries. The review detects general trends and differences in the sets of variables and the content of country samples. Moreover, this paper highlights the problem of “small countries bias” in the reviewed studies: situation when “small” (in terms of national innovation system scope and the level of development) countries (like Venezuela, Kyrgyzstan etc.) are included in the country sample, these “small” countries become the efficient ones. In general, empirical studies on cross-country analysis of national innovation systems efficiency using DEA method pay little attention to profound analysis of previous relevant studies. Therefore, this paper is among the first papers with deep review of such empirical studies

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File URL: http://www.hse.ru/data/2013/08/01/1291266138/16STI2013.pdf
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Paper provided by National Research University Higher School of Economics in its series HSE Working papers with number WP BRP 16/STI/2013.

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Length: 24 pages
Date of creation: 2013
Date of revision:
Publication status: Published in WP BRP Series: Science, Technology and Innovation / STI, August 2013, pages 1-24
Handle: RePEc:hig:wpaper:wpbrp16sti2013
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  1. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  2. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
  3. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
  4. Astrid Cullmann & Jens Schmidt-Ehmcke & Petra Zloczysti, 2009. "Innovation, R&D Efficiency and the Impact of the Regulatory Environment: A Two-Stage Semi-Parametric DEA Approach," Discussion Papers of DIW Berlin 883, DIW Berlin, German Institute for Economic Research.
  5. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
  6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
  7. Maxim Kotsemir, 2012. "Dynamics of Russian and World Science through the Prism of International Publications," Foresight-Russia, National Research University Higher School of Economics, vol. 6(1), pages 38-58.
  8. Ta-Wei Pan & Shiu-Wan Hung & Wen-Min Lu, 2010. "Dea Performance Measurement Of The National Innovation System In Asia And Europe," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 27(03), pages 369-392.
  9. Ekaterina Khramova & Dirk Meissner & Galina Sagieva, 2013. "Statistical patent analysis indicators as a means of determining country technological specialisation," HSE Working papers WP BRP 09/STI/2013, National Research University Higher School of Economics.
  10. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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